Predictive crop protection using machine learning: A scalable framework for U.S. Agriculture

Abstract

The increasing unpredictability of biotic stressors—such as pests, pathogens, and invasive species—poses a major threat to crop productivity, profitability, and food security across U.S. agricultural systems. Traditional crop protection approaches, often reactive and resource-intensive, struggle to cope with the dynamic interactions between environmental conditions, crop genotypes, and pathogen evolution. As the agricultural sector transitions toward climate-resilient and precision-based farming systems, there is a growing imperative for scalable, data-driven solutions that can anticipate disease outbreaks and optimize interventions before yield losses occur. This study proposes a machine learning (ML)-based framework for predictive crop protection that integrates multi-source agricultural datasets including satellite imagery, IoT sensor data, weather forecasts, and historical disease incidence records. Using supervised learning algorithms—such as random forests, support vector machines, and LSTM neural networks—the framework is trained to identify high-risk spatiotemporal patterns in pest and disease proliferation. The resulting predictive models are embedded into a modular decision-support platform accessible to farmers, agronomists, and policymakers. Real-world case studies from U.S. corn, soybean, and wheat production systems demonstrate the framework’s ability to deliver early-warning alerts, reduce unnecessary pesticide usage, and support site-specific treatment recommendations. The model’s scalability, interoperability with existing farm management platforms, and explainability make it suitable for widespread adoption. The framework aligns with USDA’s goals for sustainable intensification, economic efficiency, and environmental protection in modern agriculture.

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